mike210

Interesting - what kinds of tasks do you reach for 3.5?

I sometimes but rarely turn on Max for Gemini for more context in a long conversations. The tool use (5 cents per tool call) can get pretty ridiculous on Claude 3.7 Sonnet Max and I've had calls that have been ~$2.

Hi guys - glad to see this! I agree it's a huge problem and one that I've felt several times.

The reason why I never book on TaskRabbit or Thumbtack anymore is because there's legitimately an 80% chance that they don't show up. There's no penalty for not showing up or expressing higher availability and just choosing from it.

This means that every repair or home service that I consider is a simple question. Is this worth rebooking multiple times or will I just bite the bullet and try to do it myself.

I would be willing to pay around 1.5 - 2X more than I book for if I had some guarantee - financial or otherwise, that they would show up. Just the ability to construct a simple equation in my head - "is this repair worth $X" in my head without trying to manage the cost of a no-show would be huge and would have me checking the cost of much more small repairs.

Hi! First, your last sentence made us really happy - thanks for the kind words.

As for your points: 1. Definitely. We are trying to get to feature parity ASAP with traditional methods so that our AI modules can really shine. We're building out a library right now so that scientists can put together their own pipelines.

2. We're keeping everything open at this point, and definitely see the possibility that other domains will need structured image analysis like the kind that we are building.

3. Interesting - might be a better way to phrase what we are working on.

Thanks! If you're interested in chatting, would love to get in touch. Reach out at michael at biodock dot ai.

Hey! What kind of histology? If you mean doing AI analysis of histology images for research/etc. Then 100% - that's actually of the modules we're looking to do and if you work with those kind of images I would love for you to reach out at michael at biodock dot ai. We're totally free for academics, and a module like that would be free for academics as well.

If you mean clinical diagnostics histology - we're going to hold off a bit on that, although we're discussing some early partnerships there.

Also would love to chat if you ping me about the automation part - would love to get some feedback there. michael at biodock dot ai

Interesting - it would be great to chat and find out more. Maybe there are things we can learn about each other. Can you shoot me an email at michael at biodock dot ai?

Hey! Super cool. Will shoot you a message. Would love to chat and see what things we could talk about. Seems like you guys have built out some valuable assay analyses that we've heard people asking for, so congrats.

Hi Andy,

We do have lots of unlabelled data, and we're also labeling a large portion of it. We do transfer learn for all of the models we're training, and the first backbone we use is partially self-supervised. Seems to help in overall performance, but it's not a huge effect in our experience.

Maybe once we get a lot of models we can release the backbone weights for nuclear segmentation or at least a competition set of some data we've labeled. Some IP issues here though.

What kind of alternative are you looking for? Specifically one for cells, or just for biologics in general? I'm guessing you're trying to have a better base of weights to transfer off so you can train your own model?

I would say we have medium diversity in terms of images - I think unless you have a similar application right now, you'd be better off transferring off of Imagenet just due to the amount of labeled data.

Ah, yes. We're even looking to use some generative models in order to even do variations based on data and then compare that we do similarly well between cases.

I guess the point I was making was that we want to make sure we don't then use this generated or modified data in order to test other algorithms in the space and say we're better. Simply put, it would be unfair for us to make changes to perform better on a hurdle and then put other algorithms through those hurdles. But for internal use, it's definitely great!